util.py

import cv2
import numpy as np
import matplotlib.pyplot as plt

MAX_WIDTH = 1000
Min_Area = 2000
SZ = 20
PROVINCE_START = 1000
"""
该文件包含读文件函数
取零值函数
矩阵校正函数
颜色判断函数
"""

# plt显示彩色图片
def plt_show_color(img):
    #cv2与plt的图像通道不同：cv2为[b,g,r];plt为[r, g, b]
    b,g,r = cv2.split(img)
    img = cv2.merge([r, g, b])
    plt.imshow(img)
    plt.show()
    
# plt显示灰度图片
def plt_show_gray(img):
    plt.imshow(img,cmap='gray')
    plt.show()

def img_read(filename):
    return cv2.imdecode(np.fromfile(filename, dtype=np.uint8), cv2.IMREAD_COLOR)
    # 以uint8方式读取filename 放入imdecode中，cv2.IMREAD_COLOR读取彩色照片


def find_waves(threshold, histogram):
    up_point = -1  # 上升点
    is_peak = False
    if histogram[0] > threshold:
        up_point = 0
        is_peak = True
    wave_peaks = []
    for i, x in enumerate(histogram):
        if is_peak and x < threshold:
            if i - up_point > 2:
                is_peak = False
                wave_peaks.append((up_point, i))
        elif not is_peak and x >= threshold:
            is_peak = True
            up_point = i
    if is_peak and up_point != -1 and i - up_point > 4:
        wave_peaks.append((up_point, i))
    return wave_peaks


def point_limit(point):
    if point[0] < 0:
        point[0] = 0
    if point[1] < 0:
        point[1] = 0


def accurate_place(card_img_hsv, limit1, limit2, color):
    row_num, col_num = card_img_hsv.shape[:2]
    xl = col_num
    xr = 0
    yh = 0
    yl = row_num
    row_num_limit = 21
    col_num_limit = col_num * 0.8 if color != "green" else col_num * 0.5  # 绿色有渐变
    for i in range(row_num):
        count = 0
        for j in range(col_num):
            H = card_img_hsv.item(i, j, 0)
            S = card_img_hsv.item(i, j, 1)
            V = card_img_hsv.item(i, j, 2)
            if limit1 < H <= limit2 and 34 < S and 46 < V:
                count += 1
        if count > col_num_limit:
            if yl > i:
                yl = i
            if yh < i:
                yh = i
    for j in range(col_num):
        count = 0
        for i in range(row_num):
            H = card_img_hsv.item(i, j, 0)
            S = card_img_hsv.item(i, j, 1)
            V = card_img_hsv.item(i, j, 2)
            if limit1 < H <= limit2 and 34 < S and 46 < V:
                count += 1
        if count > row_num - row_num_limit:
            if xl > j:
                xl = j
            if xr < j:
                xr = j
    return xl, xr, yh, yl


def img_findContours(img_contours):
    contours, hierarchy = cv2.findContours(img_contours, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
    contours = [cnt for cnt in contours if cv2.contourArea(cnt) > Min_Area]
    # print("findContours len = ", len(contours))
    # 排除面积最小的点
    car_contours = []
    for cnt in contours:
        ant = cv2.minAreaRect(cnt)
        width, height = ant[1]
        if width < height:
            width, height = height, width
        ration = width / height

        if 2 < ration < 5.5:
            car_contours.append(ant)
            box = cv2.boxPoints(ant)

    return car_contours

#进行矩形矫正
def img_Transform(car_contours, oldimg, pic_width, pic_hight):
    car_imgs = []
    for car_rect in car_contours:  # [((264.3843688964844, 259.8280334472656), (35.244625091552734, 134.99066162109375), -89.32596588134766)]
        if -1 < car_rect[2] < 1:
            angle = 1
            # 对于角度为-1 1之间时，默认为1
        else:
            angle = car_rect[2]
        car_rect = (car_rect[0], (car_rect[1][0] + 5, car_rect[1][1] + 5), angle)
        box = cv2.boxPoints(car_rect)

        heigth_point = right_point = [0, 0]
        left_point = low_point = [pic_width, pic_hight]
        for point in box:
            if left_point[0] > point[0]:
                left_point = point
            if low_point[1] > point[1]:
                low_point = point
            if heigth_point[1] < point[1]:
                heigth_point = point
            if right_point[0] < point[0]:
                right_point = point
        # x1 = int(car_rect[0][0])
        # y1 = int(car_rect[0][1])
        # x2 = x1 + int(car_rect[1][0])
        # y2 = y1 + int(car_rect[1][1])
        # car_img = oldimg[x1:x2, y1:y2]
        if left_point[1] < right_point[1]:  # 正角度
            # print('left')
            new_right_point = [right_point[0], heigth_point[1]]
            pts2 = np.float32([left_point, heigth_point, new_right_point])  # 字符只是高度需要改变
            pts1 = np.float32([left_point, heigth_point, right_point])
            M = cv2.getAffineTransform(pts1, pts2)
            dst = cv2.warpAffine(oldimg, M, (pic_width, pic_hight))
            point_limit(new_right_point)
            point_limit(heigth_point)
            point_limit(left_point)
            # print(dst.shape)
            car_img = dst[int(left_point[1]):int(heigth_point[1]), int(left_point[0]):int(new_right_point[0])]
            # print('left_point heigth_point left_point new_right_point')
            # print(int(left_point[1]), int(heigth_point[1]), int(left_point[0]), int(new_right_point[0]))
            car_imgs.append(car_img)
        # dst = cv2.warpAffine(oldimg, M, (pic_width, pic_hight))

        elif left_point[1] >= right_point[1]:  # 负角度
            # print('right')
            new_left_point = [left_point[0], heigth_point[1]]
            pts2 = np.float32([new_left_point, heigth_point, right_point])  # 字符只是高度需要改变
            pts1 = np.float32([left_point, heigth_point, right_point])
            M = cv2.getAffineTransform(pts1, pts2)
            dst = cv2.warpAffine(oldimg, M, (pic_width, pic_hight))
            point_limit(right_point)
            point_limit(heigth_point)
            point_limit(new_left_point)
            # print(dst.shape)
            car_img = dst[int(right_point[1]):int(heigth_point[1]), int(new_left_point[0]):int(right_point[0])]
            # print('right_point heigth_point new_left_point right_point')
            # print(int(right_point[1]), int(heigth_point[1]), int(new_left_point[0]), int(right_point[0]))  # 172 172 20 172
            car_imgs.append(car_img)

    return car_imgs


def img_color(card_imgs):
    colors = []
    for card_index, card_img in enumerate(card_imgs):

        green = yello = blue = black = white = 0
        try:
            card_img_hsv = cv2.cvtColor(card_img, cv2.COLOR_BGR2HSV)
        except:
            # raise ValueError("矫正矩形出错, 转换失败")
            return '', []
        # 有转换失败的可能，原因来自于上面矫正矩形出错

        if card_img_hsv is None:
            continue
        row_num, col_num = card_img_hsv.shape[:2]
        card_img_count = row_num * col_num

        for i in range(row_num):
            for j in range(col_num):
                H = card_img_hsv.item(i, j, 0)
                S = card_img_hsv.item(i, j, 1)
                V = card_img_hsv.item(i, j, 2)
                if 11 < H <= 34 and S > 34:
                    yello += 1
                elif 35 < H <= 99 and S > 34:
                    green += 1
                elif 99 < H <= 124 and S > 34:
                    blue += 1

                if 0 < H < 180 and 0 < S < 255 and 0 < V < 46:
                    black += 1
                elif 0 < H < 180 and 0 < S < 43 and 221 < V < 225:
                    white += 1
        color = "no"

        limit1 = limit2 = 0
        if yello * 2 >= card_img_count:
            color = "yello"
            limit1 = 11
            limit2 = 34  # 有的图片有色偏偏绿
        elif green * 2 >= card_img_count:
            color = "green"
            limit1 = 35
            limit2 = 99
        elif blue * 2 >= card_img_count:
            color = "blue"
            limit1 = 100
            limit2 = 124  # 有的图片有色偏偏紫
        elif black + white >= card_img_count * 0.7:
            color = "bw"
        colors.append(color)
        card_imgs[card_index] = card_img

        if limit1 == 0:
            continue
        xl, xr, yh, yl = accurate_place(card_img_hsv, limit1, limit2, color)
        if yl == yh and xl == xr:
            continue
        need_accurate = False
        if yl >= yh:
            yl = 0
            yh = row_num
            need_accurate = True
        if xl >= xr:
            xl = 0
            xr = col_num
            need_accurate = True

        if color == "green":
            card_imgs[card_index] = card_img
        else:
            card_imgs[card_index] = card_img[yl:yh, xl:xr] if color != "green" or yl < (yh - yl) // 4 else card_img[
                                                                                                           yl - (
                                                                                                                   yh - yl) // 4:yh,
                                                                                                           xl:xr]

        if need_accurate:
            card_img = card_imgs[card_index]
            card_img_hsv = cv2.cvtColor(card_img, cv2.COLOR_BGR2HSV)
            xl, xr, yh, yl = accurate_place(card_img_hsv, limit1, limit2, color)
            if yl == yh and xl == xr:
                continue
            if yl >= yh:
                yl = 0
                yh = row_num
            if xl >= xr:
                xl = 0
                xr = col_num
        if color == "green":
            card_imgs[card_index] = card_img
        else:
            card_imgs[card_index] = card_img[yl:yh, xl:xr] if color != "green" or yl < (yh - yl) // 4 else card_img[
                                                                                                           yl - (
                                                                                                                   yh - yl) // 4:yh,
                                                                                                           xl:xr]

    return colors, card_imgs


# 根据设定的阈值和图片直方图，找出波峰，用于分隔字符
def find_waves(threshold, histogram):
    up_point = -1  # 上升点
    is_peak = False
    if histogram[0] > threshold:
        up_point = 0
        is_peak = True
    wave_peaks = []
    for i, x in enumerate(histogram):
        if is_peak and x < threshold:
            if i - up_point > 2:
                is_peak = False
                wave_peaks.append((up_point, i))
        elif not is_peak and x >= threshold:
            is_peak = True
            up_point = i
    if is_peak and up_point != -1 and i - up_point > 4:
        wave_peaks.append((up_point, i))
    return wave_peaks

#分离车牌字符
def seperate_card(img, waves):
    part_cards = []
    for wave in waves:
        part_cards.append(img[:, wave[0]:wave[1]])

    return part_cards


def img_mser_color(card_imgs):
    colors = []
    for card_index, card_img in enumerate(card_imgs):
        green = yello = blue = black = white = 0
        card_img_hsv = cv2.cvtColor(card_img, cv2.COLOR_BGR2HSV)
        if card_img_hsv is None:
            continue
        row_num, col_num = card_img_hsv.shape[:2]
        card_img_count = row_num * col_num
        for i in range(row_num):
            for j in range(col_num):
                H = card_img_hsv.item(i, j, 0)
                S = card_img_hsv.item(i, j, 1)
                V = card_img_hsv.item(i, j, 2)
                if 11 < H <= 34 and S > 34:
                    yello += 1
                elif 35 < H <= 99 and S > 34:
                    green += 1
                elif 99 < H <= 124 and S > 34:
                    blue += 1
                if 0 < H < 180 and 0 < S < 255 and 0 < V < 46:
                    black += 1
                elif 0 < H < 180 and 0 < S < 43 and 221 < V < 225:
                    white += 1
        color = "no"
        if yello * 2 >= card_img_count:
            color = "yello"

        elif green * 2 >= card_img_count:
            color = "green"

        elif blue * 2 >= card_img_count:
            color = "blue"

        elif black + white >= card_img_count * 0.7:
            color = "bw"
        colors.append(color)
        card_imgs[card_index] = card_img
    return colors, card_imgs